What is the difference between assisted and unassisted learning in relation to AI?
Although there doesn't seem to be standardization of the terminology involving assisted learning, I've been noticing this concept, and the distinction, popping up in articles recently, surely driven by important milestones in unassisted machine learning.


My understanding is that Algorithmic Intelligence has traditionally been based on complex decision-making algorithms that are trained to some degree by the programmers.  (In essence, given a bit of a head start by utilizing human knowledge in regard to the problem.)  
By contrast, unassisted machine learning is an Algorithmic Intelligence that learns only by its own analysis of models and problems.  No human knowledge goes into the automata, which develops intelligence entirely on its own.  


A passage from Matthew Lai's Giraffe Chess paper may provide some insight:


  "This report presents Giraffe, a chess engine that uses self-play to discover all its
  domain-specific knowledge, with minimal hand-crafted knowledge given by the programmer.
  Unlike previous attempts using machine learning only to perform parametertuning
  on hand-crafted evaluation functions, Giraffe’s learning system also performs
  automatic feature extraction and pattern recognition. The trained evaluation function
  performs comparably to the evaluation functions of state-of-the-art chess engines - all of
  which containing thousands of lines of carefully hand-crafted pattern recognizers, tuned
  over many years by both computer chess experts and human chess masters."


DeepMind, for instance, is setting automata on video games and letting the automata learn with no human guidance whatsoever, and getting very good results.

As to why unassisted machine learning is worrisome relates to the issue of human/AI value alignment and the control problem: if we don't know what an an automata is thinking, or even how smart it has become, how can homo sapiens sapiens be assured of positive outcomes for our species?